Machines such as, for example, track-type tractors, dozers, motor graders, wheel loaders, and the like, are used to perform a variety of tasks. For example, these machines may be used to move material and/or alter work surfaces at a worksite. In general, the machines may function in accordance with a work plan for a given worksite to perform operations, including digging, loosening, carrying, and any other manipulation of material within a worksite. Furthermore, the work plan may often involve repetitive tasks that may be entirely or at least partially automated to minimize operator involvement. Accordingly, the machines may include not only manned machines, but also autonomous or semi-autonomous vehicles that perform tasks in response to preprogrammed commands or commands delivered remotely and/or locally.
In such work environments, it is desirable to ensure that the machines perform work operations such that the material is moved in an efficient and productive manner. In substantially automated work environments, much of the overall efficiency or productivity relies on the predictability of each machine, or the ability of the machine to accurately execute the task according to the predetermined work plan. In dozing applications, the ability of the machine to accurately initiate a cut at the appropriate target cut location for a given pass can be adversely affected by inconsistencies in the materials involved, irregularities in the work surface, machine limitations, or a variety of other factors. Moreover, seemingly insignificant deviations in the initial cut position may be compounded and pronounced after several passes, which may require more time and effort to correct at the back end.
Realizing the significance of providing more accurate cuts, conventional autonomous dozing systems attempt to prevent such deviations at the forefront. More particularly, several conventional systems employ sensors or other feedback mechanisms installed on the machines to closely monitor the actual progress relative to the planned cut profile and adjust machine and implement controls to minimize deviations. As disclosed in U.S. Pat. No. 8,731,784 (“Hayashi”), for example, a laser guided mechanism is used to provide feedback of a cutting blade edge position relative to the actual surface or terrain and to adjust the blade position accordingly. While such systems may help prevent the potential for initial errors, these systems do not provide adequate means for reacting to missed target cut points if and when they do occur.
Accordingly, there is a need to provide more intuitive and systematic means for reacting to missed cuts in a manner which aids in improving overall efficiency and productivity. The present disclosure is directed at addressing one or more of the deficiencies and disadvantages set forth above. However, it should be appreciated that the solution of any particular problem is not a limitation on the scope of this disclosure or of the attached claims except to the extent express noted.